The Stochastic Coefficients Approach to Econometric Modeling, Part III: Estimation, Stability Testing, and Prediction

P.A.V.B. Swamy, Roger K. Conway and Michael LeBlanc

Journal of Agricultural Economics Research, 1988, vol. 41, issue 01, 17

Abstract: In this final article of our three-part series, we demonstrate why stochastic coefficients models are well suited to predict future variables We analyze the forecasting problem and consider various criteria of prediction If a forecaster must choose one from among several coherent predictors, then the choice should be the one with the best track record Decomposing the forecast error shows that stochastic coefficients models can cover more possible sources of prediction error and correct for them The empirical record shows that stichastic coefficients models can substantially reduce out-of-sample forecast errors more than fixed coefficients models Our assessment of coefficient stability tests is they are contradictory , misleading, and without empirical value

Keywords: Financial Economics; Research Methods/Statistical Methods; Risk and Uncertainty (search for similar items in EconPapers)
Date: 1988
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/137668/files/Swamy_41_1.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:uersja:137668

DOI: 10.22004/ag.econ.137668

Access Statistics for this article

More articles in Journal of Agricultural Economics Research from United States Department of Agriculture, Economic Research Service Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().